3D Shape Segmentation: A Review

Title:3D Shape Segmentation: A Review

Volume: 16 Issue: 5

Author(s): Rui Li and Qingjin Peng*

Affiliation:

• Department of Mechanical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, Canada

Keywords: 3D modeling, shape segmentation, shape analysis, feature extraction, mesh model, point clouds.

Abstract:

Background: Shape segmentation is commonly required in many engineering fields to separate a 3D shape into pieces for some specific applications. Although there are different methods proposed to segment the 3D shape, there is a lack of analyses of their efficiency and accuracy. It is a challenge to select an effective method to meet a particular requirement of the shape segmentation.

Objective: This paper reviews existing methods of the shape segmentation to summarize the methods and processes to identify their pros and cons.

Methods: The process of the shape segmentation is summarized in two steps: feature extraction and model separation.

Results: Shape features are identified from the available methods. Different methods of the shape segmentation are evaluated. The challenge and trend of the shape segmentation are discussed.

Conclusion: Clustering is the most used method for shape segmentation. Machine learning methods are a trend of 3D shape segmentations for identification, analysis and reconstruction of large-scale models.

Cite this article as:

Li Rui and Peng Qingjin *, 3D Shape Segmentation: A Review, Recent Patents on Engineering 2022; 16 (5) : e210422191089 . https://dx.doi.org/10.2174/1872212115666210203152106